This invention relates generally to expert systems and more particularly to an expert system for the design of protocols.
The statistical methods of multivariable testing, also known as Design of Experiments (DOE), have been used in industrial process design for decades. However, it has not been embraced in the scientific community despite the significant advantages these techniques offer. One notable explanation for this is that DOE methods are perceived as formidably complex.
Scientists commonly design experiments using the traditional one-variable at a time approach. More specifically, all but one variable are held constant while the one under investigation is varied. The test variable is then fixed at some xe2x80x9cgoodxe2x80x9d value based on the results and another variable is modified.
This traditional method can be costly in both time and physical resources, particularly in cases where there is a wide variability in assay precision and linearity (a direct relationship) between variables. Additionally the traditional method does not evaluate the interactions among variables.
The statistical methods of DOE are very powerful techniques that can significantly enhance the effectiveness of an experimental design. DOE methods can simultaneously consider interactions between many variables. DOE matrices can reduce the number of test configurations, reduce defects, improve experimental times, reduce expenses, improve the quality of experimental results, and greatly increase the odds of identifying hard-to-find solutions to difficult quality problems. Thus it can be seen that DOE methods are cost effective in both time and physical resources. Further, quality experimental information translates into more reliable decisions and, ultimately, shorter times to product introductions.
Genichi Taguchi carried out significant research with DOE techniques in the late 1940""s. His effort has been to make this powerful experimental technique more user friendly and apply it to improve the quality of manufactured products. Most of Taguchi""s orthogonal arrays are easier-to-use rearrangements of earlier DOE designs. Interactions can be designed in and analyzed more easily, and the arrays can be modified for mixed-level designs with simple-to-follow steps.
Additionally, classical DOE does not specifically address quality. DOE using the Taguchi approach attempts to improve quality, which is defined as the consistency of performance. The prime motivation behind the Taguchi experiment design technique is to achieve reduced variation, also known as robust design. Robust designs, using ideas derived from Taguchi, allow the user to simultaneously study the controllable factors and reduce the effect of uncontrollable environmental variables. This technique, therefore, is focused on attaining the desired quality objectives in all steps.
Dr. Taguchi""s standardized version of DOE, popularly known as the Taguchi method or Taguchi approach, was introduced in the USA in the early 1980""s. Because of their simplicity and success in industrial process design, the Taguchi methods offer a cost-effective strategy involving interactions between wide ranging variable combinations. Today it is one of the most attractive quality building tools used by all types of engineers in the manufacturing industries.
The Taguchi philosophy of design of products and/or processes identifies three design stages: systems design, parameter design, and tolerance design. In the first stage, systems design, the designer draws upon his/her knowledge of the process in question to produce an initial design of a product or process. The use of experimentation may be irrelevant during this phase, but will become an essential element at the next stage, parameter design. The objective of parameter design is to choose suitable values for the parameters of the product or process. In the third stage, tolerance design, inexpensive components are replaced by better ones to achieve quality within the desired tolerance.
Current DOE tools assume that the user will have sufficient information to effectively define the first two stages; systems design and parameter design. That is, that the user is familiar with the nuances of the particular plan of the scientific experiment or treatment (protocol method) being employed. They also assume that the user will be able to select appropriate constants, variables, and variable value ranges.
In addition, the user is expected to have a sophisticated knowledge of statistical design and analysis; many programs provide no guidance in the analysis of the results. Often, the user is presented with a lot of statistical output that requires substantial effort to translate results data into relevant answers.
In sum, the use of DOE tools can be complex, daunting, and can require a significant amount of time and effort to master.
These limitations make the use of current DOE design applications unattractive to those who could otherwise make productive use of these powerful statistical design a protocol.
Other inventors have created several types of expert systems for protocol design, employing DOE methods. However, none integrate a combination of a simple user interface, measurement unit conversion tools, specialized learning knowledge bases, a data structure for storing user tested protocol methods, a rule set which is used to process saved data and incorporate it into the knowledge bases, a hierarchy of parameter selection rules, robust experimental design and analysis tools, display the experiment design analysis in a way which is easily understood, and a feedback method for the refinement of the protocol method.
In addition many of these inventions are of such a sophisticated nature that their implementation is limited to hardware and software systems with specialized tools and are thus limited to a small group of users who have access to such facilities. U.S. Pat. No. 4,472,770 (Li, Sep. 18, 1984), U.S. Pat. No. 4,710,864 (Li Dec. 1, 1987) and U.S. Pat. No. 4,910,600 (Li Mar. 20, 1990), xe2x80x9cSelf-optimizing method and machinexe2x80x9d, make use of statistical design matrix for automated experiment design and testing an object but assumes that this object is well defined, determines the test designs without human control or interaction, and does not integrate a knowledge base, nor does it have the ability to save results for future reference by others.
Patent JP7200662 (Hiroko, Aug. 04, 1995), xe2x80x9cExperiment Plan Production Support Systems Having Design Knowledge Basexe2x80x9d, requires that the product, the results of a completed process, has already been generated and that the relevant parameters of the initial process that produced the resulting product are known. These results are required before the experiment plan can be generated. In addition, it does not provide for an feedback loop.
U.S. Pat. No. 5,107,499 (Lirov, et al. Apr. 21, 1992), xe2x80x9cArrangement for automated troubleshooting using selective advice and a learning knowledge basexe2x80x9d, interactively communicates between a user and utilizes a learning knowledge base but it does so in a complex fashion and does not incorporate DOE design methods.
U.S. Pat. No. 5,253,331 (Lorenzen, et al. Oct. 12, 1993), xe2x80x9cExpert system for statistical design of experimentsxe2x80x9d, defines a method for interacting with a user to specify an experimental design. However, it does not utilize a knowledge base, nor provide a feedback method after the experiment has been completed, and involves complex interactions between multiple layers of programming language tools and is thus is restrictive in the type of computer hardware and software platforms on which it can be developed.
xe2x80x9cStatistics in Research and Developmentxe2x80x9d, Second Edition, Ronald Caulcutt, Chapman and Hall, 1991
Cobb, B. D. and J. M. Clarkson (1994), xe2x80x9cA Simple Procedure for Optimizing the Polymerase Chain Reaction (PCR) Using Modified Taguchi Methods,xe2x80x9d Nucleic Acids Research, Vol. 22, No. 18, pp. 3801-3805.
Briones, P., xe2x80x9cExperimental Design: A useful Tool for PCR Optimizationxe2x80x9d, BioTechniques, 21:134-140
xe2x80x9cWhy Don""t More Researchers Use Design of Experiments?xe2x80x9d, RandD Magazine, January 1995, pp. 31
xe2x80x9cDOE Makes Research Pay Offxe2x80x9d, RandD Magazine, April 1997, pp. 43
In accordance with the present invention, an expert system is provided which is comprised of a simple user interface, flexible and specialized learning knowledge bases, data structures and process for storing user tested protocol methods, hierarchy of parameter selection rules, measurement unit conversion tools, robust experimental design and analysis tools, display of the experiment design analysis in a way which is easily understood, and an optional feedback method for the refinement of the protocol method.
Accordingly, several objects and advantages of the present invention are that it:
Has a simple design for ease of use; it requires the user to complete only a few straightforward selection steps.
Allows the user to store tested protocol design results in a specialized knowledge base which can later be accessed by others.
Allows the user to easily identify standard and previously optimized protocol method constants, variables, and variable ranges by the use of a learning knowledge base. Thus an inexperienced user can efficiently make use of the experience of others which is stored in the knowledge base.
Requires little previous experience with statistics to utilize powerful statistical optimization tools. Thus it creates an opportunity for users who would normally not take advantages of powerful DOE tools because of a lack of the time and/or skills to do so.
Has the flexibility of allowing the user to input and test novel protocol methods, constants, variables, and variable ranges and to add them to the shared knowledge base.
Allows dependent variables with linear (direct) and non-linear relationships to other variables and constants can be quickly and easily identified and the appropriate values determined from the knowledge base.
Allows users to easily refine a protocol method based on previous findings using a feedback selection and repeating the experiment with the best values identified in the analysis step.
Can more easily be implemented on a wide range of current and emerging computer hardware and software systems, unlike other expert systems, because of its simplicity of design.
Other objects and advantages of this invention are that it:
Saves time and labor because it minimizes the amount of effort a user must apply to collect preliminary protocol data, design an experiment, and analyze the results.
Saves time and labor because it enables the user to draw upon work previously completed by others; it reduces duplicated efforts.
Saves time and labor because it minimizes the number of trials necessary to troubleshoot and/or optimize a protocol method.
Satisfies a need for a fast and efficient method of optimizing new and evolving laboratory protocol methods for such quickly growing and competitive industries as biotech, healthcare, and pharmaceuticals.
Takes into account the wide variation of measurement unit standards in many fields of science by incorporating convenient measurement unit conversion tools for experimental constant and variable range units to standard and non-standard values.
Has the flexibility to incorporate knowledge bases of varied structure for efficient management of a broad range of protocol data.